Real-Time Lung Tumor Tracking Using a CUDA Enabled Nonrigid Registration Algorithm for MRI

MRI-Guided Radiation Therapy Systems

MR-Guided Radiation Therapy (MRIgRT) has been made possible only due to the ingenuity and commitment of commercial radiation therapy system vendors. Unlike conventional linear accelerator systems, MRIgRT systems have had to overcome significant and previously untested techniques to integrate the MRI systems with the radiation therapy delivery systems. Each of these three commercial systems has developed different approaches to integrating their MR and Linac functions. Each has also decided on a different main magnetic field strength, from 0.35T to 1.5T, as well as different design philosophies for other systems, such as the patient support assembly and treatment planning workflow. This paper is intended to provide the reader with a detailed understanding of each system's configuration so that the reader can better interpret the scientific literature concerning these commercial MRIgRT systems.

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians’ workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.